Manager - Data Science - Analytics - Mumbai - Lower Parel - MM
Summary
Manager-level data science role at Tata Capital in Mumbai (Lower Parel): owns analytics projects end to end — from translating business problems into analytical frameworks to building statistical/ML models for credit risk, segmentation, early warning, collections and cross-sell, then scoring, deploying, and monitoring them with business and IT teams.
. Business Problem Understanding & Approach Development
• Engage with Business, Credit, Risk, Marketing, HR, and Audit teams to understand problem statements
• Participate in cross-functional discussions to understand processes and identify analytical opportunities
• Translate business requirements into structured analytical approaches and solution frameworks
2. End-to-End Project Ownership
• Own delivery of analytics projects from problem definition to implementation and monitoring
• Manage timelines, stakeholder expectations, and delivery quality
• Ensure solutions are aligned with business objectives and decision-making needs
3. Data Preparation & Variable Creation
• Extract, clean, and prepare data from multiple sources
• Perform feature engineering and create relevant variables for model development
• Ensure data quality, consistency, and readiness for analysis
4. Model Development & Analytical Solutions
• Build models for use cases such as customer segmentation, credit risk assessment, early warning signals, collections prioritization, cross-sell and propensity modelling
• Apply appropriate statistical and machine learning techniques
• Ensure models are robust, interpretable, and aligned with business use
5. Business Analysis & Insight Generation
• Conduct detailed data analysis to identify trends, patterns, and performance gaps
• Generate insights to support decision-making across lifecycle stages
• Translate analytical outputs into clear, actionable business recommendations
6. Model Scoring & Performance Tracking
• Perform regular model scoring (monthly / periodic) to classify accounts into high, medium, and low risk categories
• Track model performance and stability over time
• Identify shifts in model behavior and recommend recalibration where required
7. Implementation & Deployment Coordination
Work closely with IT and data teams to deploy models into production systems
• Ensure smooth integration of models into business processes and workflows
• Validate outputs post-deployment to ensure accuracy and usability
8. Monitoring & Continuous Improvement
• Monitor performance of deployed models and analytics solutions
• Assess whether models continue to be relevant and effective over time
• Identify improvement areas and drive enhancements based on business feedback and data trends
9. Stakeholder Communication & Presentation
• Present analysis, models, and insights to business and functional stakeholders
• Explain methodologies and outputs in a clear and structured manner
• Support decision-making through data-backed recommendations
10. Cross-Functional Collaboration
• Work closely with Business, Credit, Risk, Marketing, HR, Audit, and IT teams
• Ensure alignment between analytics solutions and operational execution
• Act as a bridge between technical analytics and business application
11. Implementation & Deployment Coordination
• Coordinate with business and technology teams for deployment and implementation of analytical solutions
• Monitor implementation progress and ensure successful integration into business processes
• Lead and guide junior team members in analytical problem solving, model development, and interpretation of business insights
• Support capability building and knowledge sharing within the analytics function